Transcript Diapositiva 1

19 - 22 September 2011
Clarion Congress Hotel Prague
Prague, Czech Republic
Brocca, L. (1), Melone, F.(1), Moramarco, T.(1),
Wagner, W. (2), Matgen, P. (3)
(1)
Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
(2)
Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Vienna, Austria
(3)
Public Research Center - Gabriel Lippmann, Belvaux, Grand-Duchy of Luxemburg
[email protected]
SPIE 2011, Prague
19-09-11
Need for soil moisture
Brocca Luca
Soil moisture is needed by
all GEO Social Benefit Areas
and was ranked the second
top priority parameter
(behind precipitation) in a
year 2010 GEO report on
"Critical Earth Observation
Priorities".
http://sbageotask.larc.nasa.gov/US-0901a_SummaryBrochure.pdf
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Flood forecasting
Brocca Luca
1
1.5
800
2
600
2.5
3
400
3.5
Qp = 870 m3/s
Rc = 0.34
200
4.5
0
5
5/12 5/12 5/12 6/12 6/12 7/12 7/12 7/12
1200 0.0 10.0 20.0 6.0 16.0 2.0 12.0 22.0 0
85
0.5
mm
Qp = 670 m3/s
1000
1
R = 0.17
discharge (cm/s)
TIBER
BASIN
Ponte Nuovo
4
c
1.5
800
2
600
2.5
3
400
3.5
4
200
4.5
0
Introduction
Purposes
Methods
5
1/6 2/6 2/6 2/6 3/6 3/6 4/6 4/6
17.30 3.30 13.30 23.30 9.30 19.30 5.30 15.30
Study area
Results
rainfall (mm/0.5h)
 Merz and Bloschl, 2009 (WRR)
 Brocca et al., 2009 (JHE), 2010 (HESS)
0.5
1000
discharge (cm/s)
Many studies highlighted the importance
of soil moisture to determine the
catchment hydrological response:
0
35
mm
Conclusions
rainfall (mm/0.5h)
1200
SPIE 2011, Prague
19-09-11
Soil moisture monitoring
Brocca Luca
REMOTE SENSING
(AMSR-E, SAR, Scatterometer,
ASCAT, SMOS, ...)
CONTINUOUS
HYDROLOGICAL
MODEL
Introduction
Purposes
GROUND
MEASUREMENTS
(TDR, gravimetric, ...)
Methods
Study area
Results
Conclusions
Spatial and temporal resolution
SPIE 2011, Prague
19-09-11
Brocca Luca
Microwave satellite sensors
ENVISAT & ERS Symposium, 2004
"Space-borne microwave
radiometers and scatterometers
have a too coarse spatial
resolution and, hence, they do
not meet spatial requirements
for hydrological applications"
(e.g. Wang et al., 2011 (HESS)).
No system provides both a high spatial and temporal resolution
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Coarse-resolution sensors
satellite
pixels
Introduction
Brocca Luca
~25 km
Typical catchment
size for
hydrological
studies.
Purposes
Methods
Study area
Results
Conclusions
Soil moisture scaling properties
PLOT SCALE
400-9000 m2
SPIE 2011, Prague
19-09-11
Brocca Luca
SMALL CATCHMENT SCALE
~50 km2
"Representative" site soil moisture (%)
50
Castel Rigone
Casale Belfiore
Val di Rosa
45
40
35
30
25
20
20
30
40
50
Mean soil moisture (%)
CATCHMENT SCALE
~250 km2
Brocca et al., 2009 (GEOD)
Brocca et al., 2010 (WRR)
Brocca et al., 2011 (JoH, mod.rev.)
Introduction
Purposes
Methods
Study area
Results
Conclusions
Available operational sensors
ASCAT (2007-...)
AMSR-E (2002-...)
Introduction
Purposes
Methods
SPIE 2011, Prague
19-09-11
Brocca Luca
SMOS (2009-...)
Windsat (2003-...)
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Purposes
Brocca Luca
Overview of the capability of coarse resolution
satellite soil moisture data (~25 km) for
hydrological applications
1. Estimation of the antecedent wetness
condition at catchment scale
2. Improvement in runoff prediction through soil
moisture data assimilation into rainfall-runoff
modelling
Two satellite soil moisture products:
ASCAT-TUWIEN and AMSRE-LPRM
Two study areas:
Central Italy and Luxembourg
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Brocca Luca
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Exponential filter
SWI ( t ) 

i
 t  ti 

SSM ti exp 
T 

 t  ti 
exp 

T


i

Brocca Luca
SWI:
t:
SSMti:
ti:
T:
Soil Water Index
time
relative Surface Soil
Moisture [0,1]
acquisition time of SSMti
characteristic time length
Wagner et al., 1999 (RSE)
SSM
SWI
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Advanced SCATterometer
• scatterometer (active microwave)
• C-band (5.7 GHz)
• VV polarization
• resolution 50/25 km
• daily coverage
• 2007 - ongoing
Brocca Luca
ASCAT
Change detection algorithm takes
account indirectly for surface
roughness and land cover variability
Wagner et al., 1999 (RSE)
http://www.ipf.tuwien.ac.at/radar/dv/ascat/
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Advanced Microwave Scanning
Radiometer
Brocca Luca
AMSR-E
• radiometer (passive microwave)
• 6.9 - 10.7 - 18.7 - 36.5 GHz
• HH and VV polarization
• 74x43 km (6.9 GHz), 14x8 (36.5 GHz),
resampled at ~25 km
• daily coverage
• 2002 - ongoing
VUA algorithm
It is based on the Land
parameter retrieval model
(LPRM) that is a threeparameter retrieval model (soil
moisture, vegetation water
content, and soil/canopy
temperature) for passive
microwave data based on a
microwave radiative transfer
model.
Owe et al., 2008 (JGR)
Introduction
Purposes
Methods
Study area
Results
Conclusions
Antecedent Wetness Conditions
SPIE 2011, Prague
19-09-11
Brocca Luca
SOIL CONSERVATION SERVICE-CURVE NUMBER
METHOD FOR ABSTRACTION
P  Fa 
2
Rd 
P  Fa  S
 1000

S  25 .4
 10 
 CN

Fa  S

1
Sobs  2 2P  Rd  Rd  2 Rd2  2Rd2  4PRd  Rd2
2
Rd: runoff depth
  0.2
  0.2
2
Sobs  5 P  2 Rd  4 Rd  5PRd 


Introduction
Purposes
Methods
Study area

P: total rainfall
Fa: initial abstraction
S: soil potential
maximum retention
l: initial abstraction
parameter (=0.2)
CN: Curve Number
Results
Conclusions
0.5
0.49
0.48
0.47
0.46
0.45
0.44
0.43
0.42
0.41
0.4
0.39
0.38
0.37
0.36
0.35
0.34
0.33
0.32
0.31
0.3
27/11/2003
Brocca Luca
1.60
observed soil moisture
discharge
rainfall
1.40
1.20
1.00
0.80
Rd
0.60
SWI
0.40
0.20
P
discharge (m³/s) and rainfall (mm/0.5h)
soil moisture (-)
Antecedent Wetness Conditions
SPIE 2011, Prague
19-09-11
0.00
27/11/2003
28/11/2003
28/11/2003
2
Sobs  5 P  2 Rd  4 Rd  5PRd 


Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Antecedent Wetness Conditions
0.8
0.5
0.4
0.4
0.4
0.3
0.3
0.3
0.2
0.2
0.2
0.6
SWI ()
0.5
0.1
0.8
0
50
SWI ()
0.7
0
50
100
150
200
T=80 days
0.7
0.8
0.6
0.5
0.5
0.5
0.4
0.4
0.4
0.3
0.3
0.3
0.2
0.2
0.2
0.1
Cerfone
0
50
100
150
200
T=41 days
0
50
100
0.8
150
200
T=73 days
0.7
0
0.5
0.5
0.5
0.4
0.4
0.4
0.3
0.3
0.3
0.2
0.2
0.2
obs
Methods
0.1
0
Australia
0
50
200
France
S
Study area
200
obs (mm)
Results
150
200
Nestore
0
150
100
T=31 days
0.1
Topino
100
50
0.7
0.6
0
Beck et al., 2010 (JSTARS)
0
50
100
150
Tramblay et al., 2010 (JoH), 2011 (NHESS)
S (mm)
150
200
T=47 days
Caina
0.8
0.6
Timia
100
0
0.6
0.1
50
0.1
Genna
0
0
0
0.7
0.6
0.1
SWI ()
0.8
Niccone
0
0.6
0.7
Purposes
Assino
0
100
150
200
T=33 days
0.8
Introduction
0.1
0.1
Tevere - PN
0
Brocca et al., 2009 (JoH)
2009 (JHE)
0.8
0.7
0.6
11 catchments
100-5000 km2
0.8
T=80 days
T=80 days
ERS SCATTEROMETER
SOIL
0.7
0.6
MOISTURE
DATA
0.5
T=45 days
0.7
Italy
Tiber
River
Brocca Luca
0
50
100
150
Sobs (mm)
Conclusions
200
Soil moisture data assimilation
SPIE 2011, Prague
19-09-11
Brocca Luca
1. Rainfall-runoff model:
MISDc
2. Linear rescaling
3. Data assimilation technique:
NUDGING SCHEME
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Rainfall-runoff model: MISDc
Brocca Luca
MISDc: "Modello Idrologico Semi-Distribuito in continuo"
EVENT-BASED
RAINFALL-RUNOFF
MODEL (MISD)
SOIL WATER BALANCE
MODEL
e(t):
evapotranspiration
upstream
discharge
r(t):
rainfall
rainfall excess
SCS-CN
S: soil potential maximum retention
W(t)/Wmax: saturation degree
s(t):
saturation
excess
Wmax
W(t)
100
W(t)
subcatchments
geomorphological IUH
S(t)
80
S (mm)
f(t):
infiltration
directly draining areas
60
linear reservoir IUH
40
outlet
discharge
20
channel routing
diffusive linear approach
0
g(t):
percolation
0.6
0.7
0.8
0.9
1
W(t)/Wmax
FREELY AVAILABLE !!!
Brocca et al., 2011 (HYP)
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Rainfall-runoff model: MISDc
Brocca Luca
CORDEVOLE
NS= 0.86371 |ErrQp|= 0.19872 |ErrVol|= 0.11296
01/01/08
ALZETTE
02/07/07
sat. deg. (%)
rain (mm/h)
01/01/05
SD
AWC
R
60
40
20
0
5
10
discharge
Qsim
(m 3s-1)
30
20
40
20
Qobs
10
Qsim
8
6
4
2
10
0
0
2
3 4 5
200
6
7
400
8
9
10
600
1112
13
800
Time (h)
14
15 16 17 18
1000
1200
19
0
5
10
12
Luxembourg
1
sat. degree
floods
rainfall
60
(m 3s-1)
Qobs
40
discharge
80
0
0
rain
(mm/h)
sat. deg. (%)
rain (mm/h)
80
rain
(mm/h)
NS= 0.83744 |ErrQp|= 0.18363 |ErrVol|= 0.16433
1
2
20
20
21 22
1400
3
40
60
80
100
Time (h)
4
120
140
160
180
North
Italy
France
01/01/09
Central
Italy
NS= 0.85256 |ErrQp|= 0.15566 |ErrVol|= 0.15953
80
sat. degree
floods
rainfall
40
20
0
01/01/90
0
50
sat. deg. (%)
rain (mm/h)
60
rain
(mm/h)
sat. deg. (%)
rain (mm/h)
01/01/08
VALESCURE
02/07/92
01/01/95
NICCONE
02/07/97
80
SD
AWC
R
60
40
20
0
0
Qobs
8
10
Qobs
6
4
2
0
1
2
3
50
4
100
5
150
6
200
7
250
Time (h)
8
300
350
9
400
10
450
(m 3s-1)
40
discharge
(m 3s-1)
discharge
Qsim
20
10
0
Introduction
Purposes
Qsim
30
Methods
Study area
1 2
3
4
100
5
6
200
7
8 9 10
11 12 13 14 15 16 17 18 19
20 21 22 23
300
400
500
600
700
800
Time (h)
Results
Conclusions
24
900
rain
(mm/h)
NS= 0.88043 |ErrQp|= 0.22062 |ErrVol|= 0.26521
SPIE 2011, Prague
19-09-11
Real time flood forecasting
Brocca Luca
Jan-2010
Flood event of
January 2010
Model implemented for
real time application for
the Umbria Region Civil
Protection Warning
System:
UPPER TIBER RIVER
http://www.cfumbria.it/
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Linear rescaling
Brocca Luca
standard deviation
 SWI ( t )  SWI ( t ) 
 SWI* ( t )  
  m od( t )   m od( t )
  SWI ( t ) 
mean
The SWI is rescaled to match the variability of the relative soil
moisture, , simulated by MISDc,  mod
 m od
1
 SWI*
relative soil moisture
0.9
0.8
0.7
0.6
SWI
0.5
0.4
0.3
0.2
0.1
0
Jan2007
May2007
Introduction
Sep2007
Jan2008
Purposes
May2008
Sep2008
Methods
Jan2009
May2009
Study area
Sep2009
Jan2010
Results
May2010
Sep2010
Jan2011
Conclusions
SPIE 2011, Prague
19-09-11
Nudging scheme
Brocca Luca
relative soil moisture
 mod ( t )
 ass ( t )
 SWI* ( t )
observations
 mod ( t )
modeled soil moisture
 ass ( t )
updated soil moisture
 SW I* (t )
assimilation time
time
ass ( t )  mod( t )  G  SWI* ( t )  mod( t )
G is a constant
G=0 "perfect" model
G=1 direct insertion
model error
Kalman GAIN
obs error
Brocca et al., 2010 (HESS)
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Study areas
Brocca Luca
Data period:
2007-2008
# Catchments:
10
Drainage area:
10-1000 km2
Data period:
2007-2010
# Catchments:
10
Drainage area:
90-900 km2
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
In situ soil moisture data
Introduction
Purposes
Methods
Study area
Brocca Luca
Results
Conclusions
SPIE 2011, Prague
19-09-11
Brocca Luca
Introduction
Purposes
Methods
Study area
Results
Conclusions
Soil moisture products validation
Italy
SPIE 2011, Prague
19-09-11
Brocca Luca
ASCAT
AMSRE-PRI
AMSRE-NASA
AMSRE-LPRM
Vallaccia
Modelled data
5 cm depth
Introduction
Purposes
Methods
Study area
Results
Conclusions
Brocca et al.,
2011 (RSE)
Introduction
Purposes
Methods
Study area
1
0
0
FR-VOB-mod
FR-PRG-obs
FR-LZC-obs
FR-URG-obs
SP-VCE-mod
SP-I06-mod
SP-I06-obs
SP-F11-mod
SP-F11-obs
SP-K10-mod
SP-K10-obs
LU-BIB-mod
LU-BIB-obs
IT-CHI-mod
IT-TOR-mod
IT-MEL-mod
IT-BAG-mod
IT-CAP-mod
IT-SPO-mod
IT-CER-mod
IT-VAL-mod
Average
R-values ~0.80
0.5
1
0.5
0.5
SWI-CDF
SSM-CDF
1
AMSRE-LPRM
0
0.5
SWI-REG
FR-VOB-mod
FR-PRG-obs
FR-LZC-obs
FR-URG-obs
SP-VCE-mod
SP-I06-mod
SP-I06-obs
SP-F11-mod
SP-F11-obs
SP-K10-mod
SP-K10-obs
LU-BIB-mod
LU-BIB-obs
IT-CHI-mod
IT-TOR-mod
IT-MEL-mod
IT-BAG-mod
IT-CAP-mod
IT-SPO-mod
IT-CER-mod
IT-VAL-mod
ASCAT
FR-VOB-mod
FR-PRG-obs
FR-LZC-obs
FR-URG-obs
SP-VCE-mod
SP-I06-mod
SP-I06-obs
SP-F11-mod
SP-F11-obs
SP-K10-mod
SP-K10-obs
LU-BIB-mod
LU-BIB-obs
IT-CHI-mod
IT-TOR-mod
IT-MEL-mod
IT-BAG-mod
IT-CAP-mod
IT-SPO-mod
IT-CER-mod
IT-VAL-mod
0
SSM-REG
Correlation
coefficient between
all satellite products
and ground data
sets
FR-VOB-mod
FR-PRG-obs
FR-LZC-obs
FR-URG-obs
SP-VCE-mod
SP-I06-mod
SP-I06-obs
SP-F11-mod
SP-F11-obs
SP-K10-mod
SP-K10-obs
LU-BIB-mod
LU-BIB-obs
IT-CHI-mod
IT-TOR-mod
IT-MEL-mod
IT-BAG-mod
IT-CAP-mod
IT-SPO-mod
IT-CER-mod
IT-VAL-mod
Soil0 moisture
products
validation
0.5
1
0
IT-SPO-mod
IT-CER-mod
IT-VAL-mod
AMSRE-NASA
Results
1
IT-SPO-mod
IT-CER-mod
IT-VAL-mod
SPIE 2011, Prague
19-09-11
Brocca
0.5 Luca
Conclusions
1
AMSRE-PRI
Antecedent Wetness Conditions
SPIE 2011, Prague
19-09-11
Brocca Luca
TIBER
RIVER
Average
T~50 days
Introduction
Purposes
Methods
Study area
Results
Conclusions
Antecedent Wetness Conditions
SPIE 2011, Prague
19-09-11
Brocca Luca
ALZETTE
RIVER
Average
T~17 days
Introduction
Purposes
Methods
Study area
Results
Conclusions
Antecedent Wetness Conditions
TIBER RIVER
SPIE 2011, Prague
19-09-11
Brocca Luca
ALZETTE RIVER
+0.14
+0.04
ASCAT outperforms AMSRE-LPRM for the estimation of the Antecedent Wetness Conditions
at catchment scale, mainly for Italian catchments.
Introduction
Purposes
Methods
Study area
Results
Conclusions
Modelled vs satellite soil moisture
SPIE 2011, Prague
19-09-11
Brocca Luca
smooth temporal pattern
High correlation
coefficient
between both
ASCAT and
AMSRE-LPRM
satellite products
with simulate soil
moisture data by
MISDc model
Niccone
Migianella
137 km2
Central Italy
2007-2010
Alzette
Hesperange
292 km2
Luxembourg
2007-2008
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Runoff simulation
Brocca Luca
NO ASSIMILATION
Niccone
Migianella
overestimation
137 km2
NS=80%
Central Italy
2007-2010
Alzette
Hesperange
292 km2
NS=86%
Luxembourg
2007-2008
NS: Nash-Sutcliffe
Efficiency Index
NS=100%  perfect model!
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Soil moisture data assimilation
Brocca Luca
ASCAT ASSIMILATION
Niccone
Migianella
improving
137 km2
NS=87%
Central Italy
2007-2010
Alzette
Hesperange
NS=85%
slightly worse
292 km2
slightly better
Luxembourg
2007-2008
NS: Nash-Sutcliffe
Efficiency Index
NS=100%  perfect model!
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Soil moisture data assimilation
Brocca Luca
AMSR-E ASSIMILATION
Niccone
Migianella
improving
137 km2
NS=86%
Central Italy
2007-2010
Alzette
Hesperange
292 km2
slightly better
NS=85%
Luxembourg
2007-2008
NS: Nash-Sutcliffe
Efficiency Index
NS=100%  perfect model!
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Data assimilation summary
Performance in
runoff prediction as
a function of G,
the Kalman Gain
Brocca Luca
NO ASSIMILATION
DIRECT INSERTION
For central
Italy, in terms
of error on
peak discharge
and runoff
volume, the
assimilation of
ASCAT soil
moisture
product
provides much
better results
than AMSRE
For Luxembourg, the impact of data assimilation is
very limited, likely due to soil freezing
Introduction
Purposes
Methods
Study area
Results
Conclusions
SPIE 2011, Prague
19-09-11
Conclusions
Brocca Luca
ASCAT-TUWIEN and AMSRE-LPRM soil moisture products are found
reliable for soil moisture estimation across Europe
ASCAT-TUWIEN and AMSRE-LPRM show good correlation with the
Antecedent Wetness Conditions estimated at catchments scale
The performance of soil moisture data assimilation for improving runoff
prediction depends on soil and climatic conditions
Soil moisture data obtained from coarse-resolution sensors can provide
useful information for hydrological applications, new important challenges
and opportunities for the use of these new sources of data in rainfallrunoff modelling are opened
SIMPLY TRY!
The proposed approaches (even improved) are going to be applied for a larger
number of catchments and regions.
Who would like to contribute by sharing rainfall-runoff and soil moisture data is
highly welcome 
Introduction
Purposes
Methods
Study area
Results
Conclusions
References cited
 Beck, H.E. et al. (2010). Improving Curve Number based storm runoff estimates using soil moisture proxies. IEEE
JSTAR, 2(4), 1939-1404.
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